{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading database at 20210303_153749...\n",
      "Database contains 13088 images\n",
      "!!!!! WARNING: This database has not been unwrapped. Analysis may not make sense! !!!!!\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append('../..')\n",
    "\n",
    "import navbench as nb\n",
    "db = nb.Database('20210303_153749')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plot all headings\n",
    "plt.plot(db.heading);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "dheading = np.diff(db.heading)\n",
    "dheading = nb.normalise180(dheading)\n",
    "\n",
    "# Plot change in heading for one place where there's a jump\n",
    "plt.plot(dheading[9500:9600]);"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "767d51c1340bd893661ea55ea3124f6de3c7a262a8b4abca0554b478b1e2ff90"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
